README.md

---
license: mit
language:
- en
size_categories:
- n<1K
tags:
- code
- cybersecurity
- vulnerability
---

# CWE-Bench-Java

<!-- Provide a quick summary of the dataset. -->

This repository contains the dataset CWE-Bench-Java presented in the paper [LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://arxiv.org/abs/2405.17238). At a high level, this dataset contains 120 CVEs spanning 4 CWEs, namely path-traversal, OS-command injection, cross-site scripting, and code-injection. Each CVE includes the buggy and fixed source code of the project, along with the information of the fixed files and functions. We provide the seed information for each CVE in this repository, as well as advisories.

## Dataset Details

### Project Identifier

In this dataset, each project is uniquely identified with a **Project Slug**, encompassing its repository name, CVE ID, and a tag corresponding to the buggy version of the project.
We show one example below:

```
DSpace__DSpace_CVE-2016-10726_4.4
^^^^^^  ^^^^^^ ^^^^^^^^^^^^^^ ^^^
|       |      |              |--> Version Tag
|       |      |--> CVE ID
|       |--> Repository name
|--> Github Username
```

All the patches, advisory information, build information, and fix information are associated with project slugs.
Since there are 120 projects in the CWE-Bench-Java dataset, we have 120 unique project slugs.
Note that a single repository may be found to have different CVEs in different versions.

### Packaged Data

```
- data/
  - project_info.csv
  - build_info.csv
  - fix_info.csv
- advisory/<project_slug>.json
```

The core set of information in this dataset lies in two files, `data/project_info.csv` and `data/fix_info.csv`.
We also provide other essential information such as CVE advisory, and build information for the projects.
We now go into the project information and fix information CSVs.

### Project Info

| id | project_slug | cve_id | cwe_id | cwe_name | github_username | github_repository_name | github_tag | github_url | advisory_id | buggy_commit_id | fix_commit_ids |
| -- | ------------ | ------ | -------|----------|-----------------|------------------------|------------|------------|-------------|-----------------|----------------|
| 1 | DSpace__DSpace_CVE-2016-10726_4.4 | CVE-2016-10726 | CWE-022 | Path Traversal | DSpace | DSpace | 4.4 | https://github.com/DSpace/DSpace | GHSA-4m9r-5gqp-7j82 | ca4c86b1baa4e0b07975b1da86a34a6e7170b3b7 | 4239abd2dd2ae0dedd7edc95a5c9f264fdcf639d |

Each row in `data/project_info.csv` looks like the example above.
We now get into each field and explain what they are.

- `id`: an integer from 1 to 120
- `project_slug`: (explained in the previous section)
- `cve_id`: a common vulnerability identifier `CVE-XXXX-XXXXX`
- `cwe_id`: a common weakness enumeration (CWE) identifier. In our dataset, there is only `CWE-022`, `CWE-078`, `CWE-079`, `CWE-094`
- `cwe_name`: the name of the CWE
- `github_username`: the user/organization that owns the repository on Github
- `github_repository_name`: the repository name on Github
- `github_tag`: the tag associated with the version where the vulnerability is found; usually a version tag
- `github_url`: the URL to the github repository
- `advisory_id`: the advisory ID in Github Security Advisory database
- `buggy_commit_id`: the commit hash (like `ca4c86b1baa4e0b07975b1da86a34a6e7170b3b7`) where the vulnerability can be reproduced
- `fix_commit_ids`: the set of commit hashes (sequentially ordered and separated with semicolon `;`) corresponding to the fix of the vulnerability

### Fix Info

The `data/fix_info.csv` file contains the fixed Java methods and classes to each CVE.
In general, the fix could span over multiple commits, and a change could be made to arbitrary files in the repository, including resources (like `.txt`, `.html`) and Java source files (including core source code and test cases).
In this table, we only include the methods and classes that are considered core.
Many of the rows in this table is manually vetted and labeled.
Note that there may be fixes on class variables, in which case there will not be method information associated with the fix.
A single function may be "fixed" by multiple commits.

Each row in `data/fix_info.csv` looks like the following.

| project_slug | cve | github_username | github_repository_name | commit | file | class | class_start | class_end | method | method_start | method_end | signature |
|--------------|-----|-----------------|------------------------|--------|------|-------|-------------|-----------|--------|--------------|------------|-----------|
| apache__activemq_CVE-2014-3576_5.10.2 | CVE-2014-3576 | apache | activemq | `00921f22ff9a8792d7663ef8fadd4823402a6324` | `activemq-broker/src/main/java/org/apache/activemq/broker/TransportConnection.java` | `TransportConnection` | 104 | 1655 | `processControlCommand` | 1536 | 1541 | `Response processControlCommand(ControlCommand)` |

- `project_slug`: the unique identifier of each project
- `cve_id`: the CVE id
- `github_username`: the user/organization that owns the repository on Github
- `github_repository_name`: the repository name on Github
- `commit`: the commit hash containing this fix
- `file`: the `.java` file that is fixed
- `class`: the name of the class that is fixed
- `class_start`, `class_end`: the start and end line number of the class
- `method`: the name of the method that is fixed
- `method_start`, `method_end`: the start and end line number of the method
- `signature`: the signature of the method. Note that we might have multiple overloaded methods with the same name but with different signatures


### Dataset Sources [optional]

An extension of this dataset can be found on the Github repository, which provides utilities to fetch and build the relevant projects, and a simple website visualizer.

- **Curated by:** Ziyang Li, Saikat Dutta 
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** MIT 

<!-- Provide the basic links for the dataset. -->

- **Repository:** [CWE-Bench-Java](https://github.com/iris-sast/cwe-bench-java/tree/master)
- **Paper [optional]:** [LLM-Assisted Static Analysis for Detecting Security Vulnerabilities](https://arxiv.org/abs/2405.17238)
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

[More Information Needed]

#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->

[More Information Needed]

### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

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[More Information Needed]

#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
Consider citing our paper:

```
@article{li2024iris,
      title={LLM-Assisted Static Analysis for Detecting Security Vulnerabilities},
      author={Ziyang Li and Saikat Dutta and Mayur Naik},
      year={2024},
      eprint={2405.17238},
      archivePrefix={arXiv},
      primaryClass={cs.CR},
      url={https://arxiv.org/abs/2405.17238},
}
```

**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->

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## More Information [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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